Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

El‐Shafai, Walid; Mohamed, Fatma A. Hossam Eldein; Elkamchouchi, Hassan M.; Abd‐Elnaby, Mohammed; ElShafee, Ahmed

doi:10.1109/access.2021.3082940

Cited by 40 publications

(11 citation statements)

References 49 publications

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“…This section discusses and explains the obtained results and security analysis of the proposed BSS model. To comprehensively assess the proposed cancelable BSS model's efficiency, different security and recognition evaluation metrics [20][21][22][23] are examined. The objective evaluation parameters that are used to evaluate the proposed BSS model are (1) correlation scores, (2) SSIM, (3) EER, (4) false positive rate (FPR), ( 5) true positive rate (TPR), (6) AROC, (7) FRR, (8) FAR, (9) mean and variance of authorized and unauthorized patterns, and (10) computational time.…”

Section: Results and Security Analysismentioning

confidence: 99%

“…In [21], several cancelable templates were generated from the same biometric by applying different chaotic maps. El-Shafai et al proposed a cancelable biometric scheme that hides all discriminative features of biometrics by employing a Genetic algorithm (GA) [22]. Initially, the GA scans a set of biometric templates.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To ensure that the proposed BSS model is highly efficient in differentiating between the licensed and unlicensed patterns, we carried out further experiments to compare the proposed model's efficiency with the authentication performance of the preceding and recent BSS models [16][17][18][19][20][21][22][23] in terms of calculating the AROC, FAR, ERR, and FRR recognition parameters. Tab.…”

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

Almomani¹,

El‐Shafai²,

Alkhayer³

et al. 2023

Computers, Materials &Amp; Continua

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Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users' biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different biometric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy. The employed AE network is unsupervised deep learning (DL) structure used in the proposed BSS model to extract main biometric features. These obtained features are utilized to generate two random chaos matrices. The first random chaos matrix is used to permute the pixels of biometric images. In contrast, the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional (2D) chaotic logistic map (CLM) algorithm. To assess the efficiency of the proposed BSS, (1) different standardized color and grayscale images of the examined fingerprint, faces, and iris biometrics were used (2) comprehensive security and recognition evaluation metrics were measured. The assessment results have proven the authentication and robustness superiority of the proposed BSS model compared to other existing BSS models. For example, the proposed BSS succeeds in getting a high area under the receiver operating characteristic (AROC) value that reached 99.97% and low rates of 0.00137, 0.00148, and 3516 CMC, 2023, vol.74, no.2 0.00157 for equal error rate (EER), false reject rate (FRR), and a false accept rate (FAR), respectively.

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Section: Results and Security Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 1 more Smart Citation

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

Almomani¹,

El‐Shafai²,

Alkhayer³

et al. 2023

Computers, Materials &Amp; Continua

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“…The database comprises Magnetic resonance imaging (MRI) normal brain, brain tumor, normal chest X-ray, COVID-19, and retinal images. These images are acquired from some public healthcare datasets [21][22][23][24], as shown in Fig. 6.…”

Section: Datasets and Computer Specificationsmentioning

confidence: 99%

Proposed Privacy Preservation Technique for Color Medical Images

El‐Shafai¹,

El-Hameed²,

El-Hag³

et al. 2023

Intelligent Automation &Amp; Soft Computing

Self Cite

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Nowadays, the security of images or information is very important. This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security. First, the secret medical image is encrypted using Advanced Encryption Standard (AES) algorithm. Then, the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit (LSB) watermarking algorithm. After that, the encrypted secret medical image with the secret report is concealed in a cover medical image, using Kekre's Median Codebook Generation (KMCG) algorithm. Afterwards, the stegoimage obtained is split into 16 parts. Finally, it is sent to the receiver. We adopt this strategy to send the secret medical image and report over a network securely. The proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (C r ), Feature Similarity Index Metric (FSIM), and Structural Similarity Index Metric (SSIM). Histogram estimation is used to confirm the matching between the secret medical image before and after transmission. Simulation results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

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“…In [11], both piecewise and logistic maps have been combined for biometric encryption, leading to high unpredictability in a highly-complicated and long-length chaotic pseudo-random number generator. In [12], the chaotic behavior of the logistic map is used for building a projection matrix of the individual biometric. This scheme is non-invertible, which fulfills the revocability, diversity, and robustness to attacks.…”

Section: Introductionmentioning

confidence: 99%

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

elazm¹,

El‐Shafai²,

Ibrahim³

et al. 2023

Intelligent Automation &Amp; Soft Computing

Self Cite

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Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data. Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems. Biometric systems provide sufficient protection and privacy for users. The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks. In addition, they have not been studied on different and large biometric datasets. Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems. This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem. The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates, and hence enhance the security and privacy of users. The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates. We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks. In addition, a Field Programmable Gate Array (FPGA) implementation of the proposed system is introduced. The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes, such as Double Random Phase Encoding (DRPE) to reveal superiority, and hence high recognition performance of the proposed cancellable biometric recognition system. The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.

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Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

Cited by 40 publications

References 49 publications

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

Proposed Biometric Security System Based on Deep Learning and Chaos燗lgorithms

Proposed Privacy Preservation Technique for Color Medical Images

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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